


'''simple docstring'''




import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a   :   Union[str, Any]												=   logging.get_logger(__name__)

_a   :   str												=   {
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}




class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  Optional[int]          =	"git_vision_model"
   def __init__(						self       :      Dict       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=768       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]=3072       ,   SCREAMING_SNAKE_CASE_       :      str=12       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=12       ,   SCREAMING_SNAKE_CASE_       :      Tuple=3       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=224       ,   SCREAMING_SNAKE_CASE_       :      Dict=16       ,   SCREAMING_SNAKE_CASE_       :      List[Any]="quick_gelu"       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=1e-5       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]=0.0       ,   SCREAMING_SNAKE_CASE_       :      int=0.0_2       ,   **SCREAMING_SNAKE_CASE_       :      Optional[int]       ,   ) ->							str:
        super().__init__(**SCREAMING_SNAKE_CASE_      )

        __snake_case           =						hidden_size
        __snake_case           =						intermediate_size
        __snake_case           =						num_hidden_layers
        __snake_case           =						num_attention_heads
        __snake_case           =						num_channels
        __snake_case           =						patch_size
        __snake_case           =						image_size
        __snake_case           =						initializer_range
        __snake_case           =						attention_dropout
        __snake_case           =						layer_norm_eps
        __snake_case           =						hidden_act





   @classmethod
   def a  (						cls       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      Union[str, os.PathLike]       ,   **SCREAMING_SNAKE_CASE_       :      List[Any]      ) ->							"PretrainedConfig":
        cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE_      )

        __snake_case      ,       __snake_case           =						cls.get_config_dict(SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )

        # get the vision config dict if we are loading from GITConfig
        if config_dict.get('model_type'      ) == "git":
             __snake_case           =						config_dict['vision_config']

        if "model_type" in config_dict and hasattr(cls       ,   'model_type'      ) and config_dict["model_type"] != cls.model_type:
             logger.warning(
                 f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                 f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'      )

        return cls.from_dict(SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )




class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  Optional[Any]          =	"git"
   def __init__(						self       :      int       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]=None       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=3_0522       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]=768       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=6       ,   SCREAMING_SNAKE_CASE_       :      Any=12       ,   SCREAMING_SNAKE_CASE_       :      Tuple=3072       ,   SCREAMING_SNAKE_CASE_       :      Dict="gelu"       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]=0.1       ,   SCREAMING_SNAKE_CASE_       :      Tuple=0.1       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=1024       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]=0.0_2       ,   SCREAMING_SNAKE_CASE_       :      str=1e-12       ,   SCREAMING_SNAKE_CASE_       :      str=0       ,   SCREAMING_SNAKE_CASE_       :      int="absolute"       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]=True       ,   SCREAMING_SNAKE_CASE_       :      int=False       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=101       ,   SCREAMING_SNAKE_CASE_       :      Dict=102       ,   SCREAMING_SNAKE_CASE_       :      str=None       ,   **SCREAMING_SNAKE_CASE_       :      Optional[int]       ,   ) ->							Tuple:
        super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_       ,   eos_token_id=SCREAMING_SNAKE_CASE_       ,   pad_token_id=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )

        if vision_config is None:
             __snake_case           =						{}
             logger.info('vision_config is None. initializing the GitVisionConfig with default values.'      )

        __snake_case           =						GitVisionConfig(**SCREAMING_SNAKE_CASE_      )
        __snake_case           =						vocab_size
        __snake_case           =						hidden_size
        __snake_case           =						num_hidden_layers
        __snake_case           =						num_attention_heads
        __snake_case           =						hidden_act
        __snake_case           =						intermediate_size
        __snake_case           =						hidden_dropout_prob
        __snake_case           =						attention_probs_dropout_prob
        __snake_case           =						max_position_embeddings
        __snake_case           =						initializer_range
        __snake_case           =						layer_norm_eps
        __snake_case           =						position_embedding_type
        __snake_case           =						use_cache
        __snake_case           =						tie_word_embeddings
        __snake_case           =						num_image_with_embedding

        __snake_case           =						bos_token_id
        __snake_case           =						eos_token_id





   def a  (						self       :      Optional[Any]      ) ->							List[Any]:
        __snake_case           =						copy.deepcopy(self.__dict__      )
        __snake_case           =						self.vision_config.to_dict()
        __snake_case           =						self.__class__.model_type
        return output






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						["""image_processor""", """tokenizer"""]
  a__       			=						"""BridgeTowerImageProcessor"""
  a__       			=						("""RobertaTokenizer""", """RobertaTokenizerFast""")
  def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Any       )     ->      Optional[int]:







    """simple docstring"""






    super().__init__(UpperCamelCase__     ,				UpperCamelCase__       )
  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Union[bool, str, PaddingStrategy] = False     ,				UpperCamelCase__     :					Union[bool, str, TruncationStrategy] = None     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					int = 0     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Optional[Union[str, TensorType]] = None     ,				**UpperCamelCase__     :					Dict     ,				)     ->      BatchEncoding:







    """simple docstring"""






    __magic_name__								=			self.tokenizer(
        text=UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				stride=UpperCamelCase__     ,				pad_to_multiple_of=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__     ,				return_attention_mask=UpperCamelCase__     ,				return_overflowing_tokens=UpperCamelCase__     ,				return_special_tokens_mask=UpperCamelCase__     ,				return_offsets_mapping=UpperCamelCase__     ,				return_length=UpperCamelCase__     ,				verbose=UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				**UpperCamelCase__     ,				)
    # add pixel_values + pixel_mask
    __magic_name__								=			self.image_processor(
        UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				do_normalize=UpperCamelCase__     ,				do_center_crop=UpperCamelCase__     ,				**UpperCamelCase__       )
    encoding.update(UpperCamelCase__       )

    return encoding
  def 				_lowercase				(					self     :					Optional[Any]     ,				*UpperCamelCase__     :					Optional[Any]     ,				**UpperCamelCase__     :					str       )     ->      str:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__     ,				**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				*UpperCamelCase__     :					int     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      str:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__     ,				**UpperCamelCase__       )





  @property
  def 				_lowercase				(					self     :					Tuple       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.tokenizer.model_input_names
    __magic_name__								=			self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )

